https://github.com/nighty3098/investingassistant
📊 Telegram is an investment assistant bot with neural network and other functions
https://github.com/nighty3098/investingassistant
ai investing investing-analysis investing-com neural-network pyrogram pyrogram-bot telegram telegram-bot tensorflow
Last synced: 8 months ago
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📊 Telegram is an investment assistant bot with neural network and other functions
- Host: GitHub
- URL: https://github.com/nighty3098/investingassistant
- Owner: Nighty3098
- License: other
- Created: 2024-10-20T14:16:49.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2025-07-01T07:02:44.000Z (11 months ago)
- Last Synced: 2025-07-01T08:23:46.691Z (11 months ago)
- Topics: ai, investing, investing-analysis, investing-com, neural-network, pyrogram, pyrogram-bot, telegram, telegram-bot, tensorflow
- Language: Python
- Homepage:
- Size: 20.8 MB
- Stars: 6
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# StockBot Documentation 📈
**Version 1.0** | **Last Updated: May 06, 2025**
---
> [!IMPORTANT]
> ONLY FOR PYTHON 3.12
## 📖 Overview
StockBot is a Telegram bot designed to provide users with stock market insights, including price predictions, news parsing, and fundamental analysis. Built using the **Pyrogram** library, the bot integrates with a deep learning model for stock price forecasting, a news parser for real-time market updates, and a database for user and stock management. The bot supports user authentication, stock tracking, and admin controls, with a token-based system for accessing premium features.
The project leverages **TensorFlow** for stock price predictions, **yfinance** for financial data, and **BeautifulSoup** for web scraping. It includes asynchronous news parsing, real-time notifications, and report generation in Excel format.
---
## 🚀 Features
- **User Management**: Register, manage, and delete user accounts with admin controls for banning/unbanning and role assignment.
- **Stock Tracking**: Add/remove stocks to a user's watchlist and retrieve detailed stock information.
- **Price Prediction**: Forecast stock prices for the next 60 days using a pre-trained deep learning model.
- **News Parsing**: Fetch and filter news from Investing.com, notifying users of articles relevant to their tracked stocks.
- **Fundamental Analysis**: Provide buy/sell recommendations based on financial metrics like P/E ratio, ROE, and risk factors.
- **Report Generation**: Generate Excel reports with stock data for user analysis.
- **Token System**: Limit access to premium features (e.g., price predictions) using a token-based system.
- **Admin Panel**: Manage users, assign admin roles, send tokens, and monitor system activity.
- **Multilingual Support**: Planned language selection (currently in development).
- **Asynchronous Processing**: Handle news parsing and notifications concurrently using asyncio.
---
## 🛠️ Requirements
To run StockBot, ensure the following dependencies are installed:
Install dependencies:
```bash
pip install -r req.txt
```
Additionally, you need:
- A Telegram Bot Token (obtained via [BotFather](https://t.me/BotFather)).
- API ID and API Hash from [my.telegram.org](https://my.telegram.org).
- A pre-trained stock prediction model (`best_model.keras`) and scaler (`stock_scaler.save`).
---
## 📂 Project Structure
```
git clone https://github.com/Nighty3098/InvestingAssistant --recurse-submodules
cd InvestingAssistant
poetry shell
poetry install
cd src
```
---
## ⚙️ Configuration
1. **Environment Variables**:
Create a `config.py` file with the following:
```python
API_ID = "your_api_id" # From my.telegram.org
API_HASH = "your_api_hash" # From my.telegram.org
BOT_TOKEN = "your_bot_token" # From BotFather
app = Client("StockBot", api_id=API_ID, api_hash=API_HASH, bot_token=BOT_TOKEN)
data_file = "data.db" # SQLite database file
log_file = "bot.log" # Log file
logger = ... # Configure logging (see main.py)
```
2. **Database**:
The bot uses SQLite (`data.db`) to store user data, stocks, and tokens. The `db.py` module handles table creation and queries.
3. **Model Files**:
Ensure `best_model.keras` and `stock_scaler.save` are in `IPSA_MODEL/price/`. These are required for price predictions.
---
## 🏃♂️ Running the Bot
1. **Start the Bot**:
Run the main script:
```bash
python main.py
```
2. **Interact with the Bot**:
- Open Telegram and start a chat with your bot.
- Use the `/start` command to initialize the bot.
- If unregistered, you'll be prompted to register.
- Registered users see the main menu; admins see the admin panel.
3. **Outputs**:
- `client_data/`: Stores generated reports (`*.xlsx`) and forecast plots (`*.png`).
- `logs/`: Contains bot logs (`bot.log`).
- `data.db`: SQLite database with user and stock data.
---
## 📱 Bot Commands and Features
### Commands
| Command | Description | Access |
|-----------------|--------------------------------------|--------------|
| `/start` | Initialize the bot and show menu | All users |
| `/send_tokens` | Send tokens to a user | Admins only |
### Callback Queries
The bot uses inline keyboards for navigation. Key actions include:
- **User Actions**:
- `register_user`: Register a new user.
- `my_stocks`: View tracked stocks.
- `add_stocks`/`remove_stocks`: Manage stock watchlist.
- `get_price`: Request a stock price prediction (requires tokens).
- `news`: Fetch news for a specified period (admin-only or in development).
- `settings`: Access settings (e.g., set city, language).
- `remove_account`: Delete user account.
- `to_main`: Return to the main menu.
- **Admin Actions**:
- `admin_panel`: Access the admin panel.
- `users_menu`: List all users with details (ID, tokens, role, status).
- `add_admin`/`rm_admin`: Add/remove admin role for a user.
- `ban_user`/`unblock_user`: Ban/unban a user.
### State Management
The bot uses a `user_states` dictionary to track user input states (e.g., `adding`, `removing`, `price`, `news`). This ensures proper handling of text inputs based on the current context.
---
## 🧠 Core Components
### 1. StockPredictor (`price_core.py`)
- **Purpose**: Predicts stock prices for the next 60 days using a pre-trained TensorFlow model.
- **Key Methods**:
- `predict_future(ticker)`: Generates 60-day price forecasts.
- `analyze(ticker, threshold=0.05)`: Provides a forecast summary with expected price change.
- `predict_plt(ticker, user_id)`: Creates a plot of historical and forecasted prices.
- **Dependencies**: TensorFlow, yfinance, Matplotlib, joblib.
- **Output**: Forecast message, price change percentage, and a plot (`client_data/forecast_{user_id}_{ticker}.png`).
### 2. NewsParser (`parsing.py`)
- **Purpose**: Scrapes news from Investing.com and filters articles relevant to user-tracked stocks.
- **Key Methods**:
- `is_stocks_in_news(url, user_id, ...)`: Checks if tracked stocks are mentioned in an article.
- `get_news_text(url)`: Extracts article text.
- `parse_investing_news(url, period, user_id)`: Parses news articles within a specified period.
- `start_parsing(period, user_id)`: Aggregates news from multiple sources.
- `check_new_articles(user_id)`: Asynchronously checks for new articles every 2 minutes.
- **Dependencies**: BeautifulSoup, requests, user-agent.
- **Output**: Formatted news messages with title, summary, URL, and price influence prediction.
### 3. AdvicePredictor (`create_report.py`)
- **Purpose**: Provides buy/sell recommendations based on fundamental analysis.
- **Key Methods**:
- `analyze_fundamentals(ticker)`: Scores stocks based on P/E ratio, ROE, debt-to-equity, revenue growth, beta, and risk metrics.
- `analyze(ticker, forecast_growth)`: Generates a recommendation with risk assessment.
- **Dependencies**: yfinance, pandas.
- **Output**: Recommendation message (e.g., "STRONG BUY", "SELL") with risk details.
### 4. ReportTable (`create_report.py`)
- **Purpose**: Generates Excel reports with stock data.
- **Key Methods**:
- `download_data(ticker)`: Fetches stock data via yfinance.
- `save_report(data)`: Saves data to an Excel file.
- **Dependencies**: pandas, yfinance.
- **Output**: Excel file (`client_data/{ticker}_report.xlsx`).
### 5. Database (`db.py`)
- **Purpose**: Manages user data, stocks, tokens, and admin roles using SQLite.
- **Key Functions** (assumed based on usage):
- `check_user_account(user_id)`: Checks if a user is registered.
- `check_user_ban(username)`: Checks if a user is banned.
- `is_admin(user_id)`: Verifies admin status.
- `get_users_stocks(user_id)`: Retrieves a user's tracked stocks.
- `update_tokens(user_id, amount)`: Updates user tokens.
- `add_city_to_db(user_id, city)`: Stores user city for timezone handling.
- **Output**: SQLite database (`data.db`).
---
## 📈 Usage Example
1. **User Interaction**:
- User sends `/start`.
- If unregistered, they click "Register" (`register_user` callback).
- Registered users see the main menu with options like "My Stocks", "Get Price", "News", and "Settings".
- To add a stock, user selects "My Stocks" → "Add Stocks", enters a ticker (e.g., "AAPL"), and confirms.
- To get a price prediction, user selects "Get Price", enters a ticker, and receives a forecast plot and report (if tokens are available).
2. **Admin Interaction**:
- Admin sends `/start` and sees the admin panel.
- They can list users (`users_menu`), ban/unban users (`ban_user`/`unblock_user`), or send tokens (`/send_tokens username tokens`).
- Admin can add/remove other admins (`add_admin`/`rm_admin`).
3. **News Notifications**:
- Users receive news updates for tracked stocks every 2 minutes (if relevant articles are found).
- News includes a title, summary, URL, and predicted price influence.
---
## 🛡️ Security and Limitations
### Security Features
- **User Authentication**: Checks user registration and ban status before granting access.
- **Admin Controls**: Restricts sensitive actions (e.g., banning users, sending tokens) to admins.
- **Token System**: Limits API-heavy features (e.g., price predictions) to prevent abuse.
- **Logging**: Comprehensive logging for debugging and monitoring (`bot.log`).
### Limitations
- **API Limits**: yfinance and Investing.com have request limits, which may cause failures during high usage.
- **News Parsing**: Limited to Investing.com sources and may miss relevant articles from other platforms.
- **Model Accuracy**: Price predictions depend on the pre-trained model's quality and may not account for sudden market events.
- **Language Support**: Multilingual support is in development and not fully implemented.
- **Error Handling**: Some errors (e.g., network failures) may not be gracefully handled for users.
---
## 📝 Notes
- **Model Dependency**: Ensure `best_model.keras` and `stock_scaler.save` are available. Train the model using the provided stock prediction script if needed.
- **Timezone Handling**: News parsing uses user-specified cities for timezone conversion. Default timezone may cause inaccuracies if not set.
- **Token Management**: Admins must manually assign tokens using `/send_tokens`. Consider automating token allocation in future updates.
- **Scalability**: For large user bases, consider optimizing database queries and news parsing with caching or parallel processing.
- **Extensibility**: Add support for more news sources, technical indicators, or real-time market data feeds.
---
## 📚 References
- Pyrogram Documentation: [https://docs.pyrogram.org/](https://docs.pyrogram.org/)
- TensorFlow Documentation: [https://www.tensorflow.org/](https://www.tensorflow.org/)
- yfinance Documentation: [https://github.com/ranaroussi/yfinance](https://github.com/ranaroussi/yfinance)
- BeautifulSoup Documentation: [https://www.crummy.com/software/BeautifulSoup/](https://www.crummy.com/software/BeautifulSoup/)
- Investing.com: [https://www.investing.com/](https://www.investing.com/)
For support, open an issue on the project repository or contact the development team.
---
**🤖 Built with Pyrogram and TensorFlow**
